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Transcription workflows, recording management, and quote extraction for journalists.
Passed 18 of 22 test cases with the skill, and 13 of 22 without it.
and 3 more in the full report
5 fixed13 passed either way4 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.
Write an FFmpeg CLI command within a Python function to extract mono WAV audio at 44.1kHz from a video interview file.
| Checked | Without skill | With skill |
|---|---|---|
| Uses -vn flag to disable video | Passed | Passed |
| Uses -acodec pcm_s16le flag | Missed | Passed |
The instructions your model reads when the skill loads.
Practical workflows for journalists managing interviews from preparation through publication.
For pre-interview research, question design, attribution agreements, and consent scripts, use the interview-prep skill. The notes here cover only the recording configuration that affects transcription quality.
# Standard recording configuration for clean transcription
RECORDING_SETTINGS = {
'format': 'wav', # Lossless for transcription
'sample_rate': 16000, # Whisper resamples to 16k anyway; 16k saves disk
'channels': 1, # Mono is fine for speech; stereo only if mics are positionally distinct
'backup': True, # Always run a backup recorder
}
# File naming convention
# YYYY-MM-DD_source-lastname_topic.wav
# Example: 2026-05-08_smith_budget-hearing.wav
Two-device rule. Always record on two devices. Phone as backup minimum. If using a wireless lav mic, the recorder built into the lav unit is one device; the phone running a backup app is the second.
Mono is preferred unless each speaker has their own dedicated microphone routed to a distinct channel. Stereo with both speakers bleeding into both channels is worse for diarization than clean mono.
test cases passed, vs 13 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.